| General | ||
|---|---|---|
| Filename(s) | sub-NDARUC804LKP_task-symbolSearch_run-1_eeg.set | |
| MNE object type | RawEEGLAB | |
| Measurement date | Unknown | |
| Participant | sub-NDARUC804LKP | |
| Experimenter | Unknown | |
| Acquisition | ||
| Duration | 00:02:38 (HH:MM:SS) | |
| Sampling frequency | 500.00 Hz | |
| Time points | 78,744 | |
| Channels | ||
| EEG | ||
| EOG | ||
| Head & sensor digitization | 131 points | |
| Filters | ||
| Highpass | 0.00 Hz | |
| Lowpass | 250.00 Hz | |
| General | ||
|---|---|---|
| Filename(s) | sub-NDARUC804LKP_task-symbolSearch_run-1_eeg.set | |
| MNE object type | RawEEGLAB | |
| Measurement date | Unknown | |
| Participant | sub-NDARUC804LKP | |
| Experimenter | Unknown | |
| Acquisition | ||
| Duration | 00:02:38 (HH:MM:SS) | |
| Sampling frequency | 500.00 Hz | |
| Time points | 78,744 | |
| Channels | ||
| EEG | ||
| EOG | ||
| Head & sensor digitization | 131 points | |
| Filters | ||
| Highpass | 1.00 Hz | |
| Lowpass | 100.00 Hz | |
| General | ||
|---|---|---|
| MNE object type | Epochs | |
| Measurement date | Unknown | |
| Participant | sub-NDARUC804LKP | |
| Experimenter | Unknown | |
| Acquisition | ||
| Total number of events | 19 | |
| Events counts | rest: 19 | |
| Time range | 0.000 – 0.500 s | |
| Baseline | off | |
| Sampling frequency | 500.00 Hz | |
| Time points | 251 | |
| Metadata | No metadata set | |
| Channels | ||
| EEG | ||
| Head & sensor digitization | 131 points | |
| Filters | ||
| Highpass | 1.00 Hz | |
| Lowpass | 100.00 Hz | |
| Method | picard |
|---|---|
| Fit parameters | ortho=False extended=True max_iter=500 |
| Fit | 359 iterations on epochs (4769 samples) |
| ICA components | 118 |
| Available PCA components | 128 |
| Channel types | eeg |
| ICA components marked for exclusion | ICA000 ICA004 ICA008 ICA011 ICA012 ICA013 ICA014 ICA021 ICA022 ICA024 ICA026 ICA030 ICA031 ICA034 ICA036 ICA037 ICA038 ICA041 ICA042 ICA044 ICA051 ICA052 ICA054 ICA056 ICA057 ICA058 ICA059 ICA062 ICA063 ICA064 ICA065 ICA068 ICA072 ICA073 ICA075 ICA077 ICA079 ICA080 ICA081 ICA082 ICA084 ICA091 ICA094 ICA096 ICA099 ICA101 ICA102 ICA104 ICA105 ICA106 ICA107 ICA108 ICA109 ICA112 ICA115 ICA116 ICA117 |
| General | ||
|---|---|---|
| MNE object type | Epochs | |
| Measurement date | Unknown | |
| Participant | sub-NDARUC804LKP | |
| Experimenter | Unknown | |
| Acquisition | ||
| Total number of events | 25 | |
| Events counts | rest: 25 | |
| Time range | 0.000 – 0.500 s | |
| Baseline | off | |
| Sampling frequency | 500.00 Hz | |
| Time points | 251 | |
| Metadata | No metadata set | |
| Channels | ||
| EEG | ||
| EOG | ||
| misc | ||
| Head & sensor digitization | 131 points | |
| Filters | ||
| Highpass | 1.00 Hz | |
| Lowpass | 100.00 Hz | |
| Projections | Average EEG reference (off) | |
No epochs exceeded the rejection thresholds. Nothing was dropped.
| Method | picard |
|---|---|
| Fit parameters | ortho=False extended=True max_iter=500 |
| Fit | 359 iterations on epochs (4769 samples) |
| ICA components | 118 |
| Available PCA components | 128 |
| Channel types | eeg |
| ICA components marked for exclusion | ICA000 ICA004 ICA008 ICA011 ICA012 ICA013 ICA014 ICA021 ICA022 ICA024 ICA026 ICA030 ICA031 ICA034 ICA036 ICA037 ICA038 ICA041 ICA042 ICA044 ICA051 ICA052 ICA054 ICA056 ICA057 ICA058 ICA059 ICA062 ICA063 ICA064 ICA065 ICA068 ICA072 ICA073 ICA075 ICA077 ICA079 ICA080 ICA081 ICA082 ICA084 ICA091 ICA094 ICA096 ICA099 ICA101 ICA102 ICA104 ICA105 ICA106 ICA107 ICA108 ICA109 ICA112 ICA115 ICA116 ICA117 |
| General | ||
|---|---|---|
| Filename(s) | sub-NDARUC804LKP_task-symbolSearch_run-1_proc-eyelink_raw.fif | |
| MNE object type | Raw | |
| Measurement date | Unknown | |
| Participant | sub-NDARUC804LKP | |
| Experimenter | Unknown | |
| Acquisition | ||
| Duration | 00:02:12 (HH:MM:SS) | |
| Sampling frequency | 500.00 Hz | |
| Time points | 65,812 | |
| Channels | ||
| EEG | ||
| EOG | ||
| misc | ||
| Head & sensor digitization | 131 points | |
| Filters | ||
| Highpass | 1.00 Hz | |
| Lowpass | 100.00 Hz | |
| General | ||
|---|---|---|
| Filename(s) | sub-NDARUC804LKP_task-symbolSearch_proc-ica_epo.fif | |
| MNE object type | EpochsFIF | |
| Measurement date | Unknown | |
| Participant | sub-NDARUC804LKP | |
| Experimenter | Unknown | |
| Acquisition | ||
| Total number of events | 23 | |
| Events counts | rest: 23 | |
| Time range | 0.000 – 0.500 s | |
| Baseline | off | |
| Sampling frequency | 500.00 Hz | |
| Time points | 251 | |
| Metadata | No metadata set | |
| Channels | ||
| EEG | ||
| EOG | ||
| misc | ||
| Head & sensor digitization | 131 points | |
| Filters | ||
| Highpass | 1.00 Hz | |
| Lowpass | 100.00 Hz | |
| Projections | Average EEG reference (on) | |
import mne
bids_root = "mergedDataset"
deriv_root = "mergedDataset/derivatives"
subjects_dir = None
#subjects = ["NDARAB678VYW","NDARDC504KWE","NDARDL033XRG","NDARTK720LER","NDARDZ794ZVP"] #"all" #["NDARAG429CGW"]
#subjects = ["NDARDZ794ZVP"] #"all" #["NDARAG429CGW"]
#subjects = ["NDARAB678VYW"] ##["NDARKM301DY0"] #"all"
#subjects = ["NDARAF535XK6"]
#subjects = ["NDARUC804LKP","NDARVD609JNZ","NDARGN483WFH","NDARFY623ZTE","NDARFN221WW5","NDARXR865BVX","NDARRK528GFZ","NDARKP823HEM","NDARYA857NDW","NDARUR298LVX","NDARWT403LP6","NDARDL033XRG","NDARYJ413BLN","NDARLP413TUX","NDARLM981MEN","NDARKG016KD1","NDARAG788YV9","NDARUA035YJN","NDARNP381RZ4","NDARZG044CJ5","NDARHT518WEM","NDARDX544FJ0","NDARKM199DXW","NDARWC905XUG","NDARYH501UH3","NDARRK146XCZ"]
subjects = ["NDARKT714TXR","NDARUC804LKP","NDARGN483WFH","NDARAL897CYV","NDARTA920XFC","NDARPW746FWF","NDARDL033XRG","NDARRK528GFZ","NDARBT436PMT","NDARZK745JGG"]
ch_types = ["eeg"]
interactive = False
sessions = []#"all"
task = "symbolSearch"
#task_et = "WISC_ProcSpeed"
task_is_rest = True
rest_epochs_duration = 5
rest_epochs_overlap = 0
runs = ["1"]
et_has_run = False
et_has_task = True
epochs_tmin = 0
#rest_epochs_duration = 10
#rest_epochs_overlap = 0
baseline = None
#baseline: tuple[float | None, float | None] | None = (-0.2, 0)
#raw_resample_sfreq: float | None = 250
eeg_reference = "average"
ica_l_freq = 1 # ?
# determined by icalabel
l_freq: float | None = 1
h_freq: float | None = 100
ica_h_freq: float | None = 100
# data was recorded in the US
notch_freq = 60
on_error = "continue"
######### Remove these when doing Unfold analysis! ############
# positive / negative feedback
#conditions = ["HAPPY", "SAD"]
##conditions = ["Fixation L"]
##
##epochs_tmin: float = -0.5
##epochs_tmax: float = 2.6 # since feedback is so infrequent, long epochs are okay
##
##baseline: tuple[float | None, float | None] | None = (-0.2, 0)
###############################################################
spatial_filter = "ica"
# ica_n_components = 96 ?
ica_algorithm = "picard-extended_infomax"
#ica_use_ecg_detection: bool = True
#ica_use_eog_detection: bool = True
ica_use_icalabel = True
#ica_reject: dict[str, float] | Literal["autoreject_local"] | None = "autoreject_local"
ica_reject = "autoreject_local" #TESTING
reject = "autoreject_local" #TESTING
#These are identical, just ensuring compatibility
sync_eyelink = True
sync_eye = True
#sync_eventtype_regex = "\\d-trigger=10 Image moves"
#sync_eventtype_regex_et = "trigger=10 Image moves"
#Contrast detection
#sync_eventtype_regex = r"contrastTrial_start"
#sync_eventtype_regex_et = r"# Message: 15"
sync_eventtype_regex = r"(?:trialResponse|newPage)" #r"trialResponse"
sync_eventtype_regex_et = r"# Message: (?:14|20)" #r"# Message: 14"
#sync_eventtype_regex = r"trialResponse"
#sync_eventtype_regex_et = r"# Message: 14"
#eog_channels = ["HEOGL", "HEOGR", "VEOGL", "VEOGU"]
#eeg_bipolar_channels = {"HEOG": ("HEOGL", "HEOGR"), "VEOG": ("VEOGL", "VEOGU")}
#eog_channels = ["HEOG", "VEOG"]
#sync_heog_ch = ("HEOG")
#eeg_bipolar_channels = {"HEOG": ("E40", "E109"),
# "VEOG": ("E21", "E127")} #left eye
##eeg_bipolar_channels = {
## #"HEOG": ("E127", "E126"),
## "HEOG": ("E126", "E127"),
## "VEOG": ("E22", "E127"), #left eye
##}
eeg_bipolar_channels = {
#"HEOG": ("E127", "E126"),
# Version 1, doesn't work well
#### "HEOG": ("E127", "E126"),
#### "VEOG": ("E22", "E127"), #left eye
# Version 2, works well but not sure why
###"HEOG": ("E109", "E40"),
###"VEOG": ("E22", "E127"),
# Version 3, seems to work well?
"HEOG": ("E2", "E26"),
"VEOG": ("E3", "E8")
}
eog_channels = ["HEOG", "VEOG"]
sync_heog_ch = "HEOG"
sync_et_ch = ("L POR X [px]", "R POR X [px]")
#sync_et_ch = "xpos_right"
sync_plot_samps = 3000
decode: bool = False
run_source_estimation = False
montage = mne.channels.make_standard_montage("GSN-HydroCel-128")
eeg_template_montage = montage
drop_channels = ["Cz"]
n_jobs = 1
Platform Linux-5.14.0-427.96.1.el9_4.x86_64-x86_64-with-glibc2.34
Python 3.11.7 (main, Aug 29 2025, 00:00:00) [GCC 11.4.1 20231218 (Red Hat 11.4.1-4)]
Executable /pfs/work9/workspace/scratch/st_st156392-mydata/mnevenv/bin/python
CPU Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz (128 cores)
Memory 251.5 GiB
Core
├☑ mne 1.10.2 (latest release)
├☑ numpy 2.3.4 (OpenBLAS 0.3.30 with 1 thread)
├☑ scipy 1.16.2
└☑ matplotlib 3.10.7 (backend=agg)
Numerical (optional)
├☑ sklearn 1.7.2
├☑ nibabel 5.3.2
├☑ pandas 2.3.3
├☑ h5io 0.2.5
├☑ h5py 3.15.1
└☐ unavailable numba, nilearn, dipy, openmeeg, cupy
Visualization (optional)
├☑ pyvista 0.46.3 (OpenGL 4.5 (Compatibility Profile) Mesa 23.3.3 via llvmpipe (LLVM 17.0.6, 256 bits))
├☑ pyvistaqt 0.11.3
├☑ vtk 9.5.2
└☐ unavailable qtpy, ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify
Ecosystem (optional)
├☑ mne-bids 0.17.0
├☑ mne-icalabel 0.8.1
├☑ mne-bids-pipeline 0.1.0.dev917+g2366e2b9a
├☑ eeglabio 0.1.2
├☑ edfio 0.4.10
├☑ pybv 0.7.6
└☐ unavailable mne-nirs, mne-features, mne-connectivity, neo, mffpy